Continuous monitoring using a predictive instrument

ABSTRACT

An instrument for continuously monitoring the condition of a patient who has a cardiovascular disease, the instrument including an electrocardiograph; a waveform analyzer which analyzes a current segment of an ECG waveform; a computer receiving output from the waveform analyzer; and a control module. The computer is programmed to complete a monitoring cycle in which the computer uses the output from the waveform analyzer to compute a probability of a life threatening cardiac condition based upon the current segment of the patient&#39;s ECG waveform. The control module causes the computer to periodically repeat the monitoring cycle and for each repetition of said monitoring cycle to compute a change-of-condition measure, wherein the change-of-condition measure is calculated by subtracting a computed probability for a previous monitoring cycle from the computed probability for the current monitoring cycle. The computer is also programmed to compare during each monitoring cycle the computed change-of-condition measure for that monitoring cycle to a threshold value and if in excess of the threshold value to generate an alarm notification.

This is a continuation of application Ser. No. 08/283,951, filed Aug. 1,1994 now U.S. Pat. No. 5,501,229.

BACKGROUND OF THE INVENTION

The invention relates to predictive instruments for computing apatient's probability of a serious cardiac condition.

A number of instruments have been developed that enable the physician tocompute probabilities of life threatening cardiac conditions forpatients. Some of these instruments are described in the followingreferences, all of which are incorporated herein be reference.

A hand-held predictive instrument is described by Michael W. Pozen etal. in "A Predictive Instrument to Improve Coronary-Care-Unit AdmissionPractices in Acute Ischemic Heart Disease" The New England Journal ofMedicine, Vol 310 pp. 1273-1278, May 17, 1984. With the handheldcalculator-based instrument, a physician can compute a patient'sprobability of having acute cardiac ischemia based uponphysician-entered values for a set of clinical variables. An automatic,computerized version of this instrument which utilizes output from aelectrocardiograph and a waveform analyzer is described by H. P. Selkeret al. in "A Time-Insensitive Predictive Instrument for Acute MyocardialInfarction Mortality", Med. Care 1991; 29:1196-1211.

A predictive instrument for determining the probability of acutehospital mortality of a cardiac patient is described in U.S. Pat. No.4,957,115 to Dr. Harry P. Selker. The probability of acute hospitalmortality is commonly understood to mean the probability of dying from acurrent acute condition, generally during the specific initialhospitalization for the problem. It is also referred to as theprobability of imminent death for the patient. That is, it is a shortterm, as opposed to a long term, probability of mortality which does notnecessarily have a precisely defined period of time associated with it.

A predictive instrument for evaluating whether to use thrombolytictherapy to treat a patient with a heart condition is described in U.S.Pat. No. 4,998,535 to Dr. Selker et al. The predictive instrumentcomputes a first probability of acute hospital mortality for the patientassuming that thrombolytic therapy is not administered and it computes asecond probability of acute hospital mortality for the patient assumingthat thrombolytic therapy is administered. The difference in thecomputed probabilities may assist the physician in deciding whether itwould be advantageous to administer the thrombolytic therapy to thepatient.

The above-mentioned predictive instruments use logistic regressionequations to model the probability that the patient has a seriouscardiac condition (e.g. the probability of acute cardiac ischemia or theprobability of imminent death from a cardiac condition).

SUMMARY OF THE INVENTION

In general in one aspect, the invention is an instrument forcontinuously monitoring the condition of a patient who has acardiovascular disease. The instrument includes an electrocardiograph; awaveform analyzer which analyzes a current segment of an ECG waveformfor the patient; a computer receiving output from the waveform analyzer;and a control module. The computer is programmed to complete amonitoring cycle in which the computer uses the output from the waveformanalyzer to compute a probability of a life threatening cardiaccondition based upon the current segment of the patient's ECG waveform.The control module causes the computer to periodically repeat themonitoring cycle and for each repetition of the monitoring cycle tocompute a change-of-condition measure. The change-of-condition measureis calculated by subtracting a computed probability for a previousmonitoring cycle from the computed probability for the currentmonitoring cycle. The computer is also programmed to compare during eachmonitoring cycle the computed change-of-condition measure for thatmonitoring cycle to a threshold value and if in excess of said thresholdvalue to generate an alarm notification.

Other advantages and features will become apparent from the followingdescription of the preferred embodiment and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a patient monitoring system constructed inaccordance with the invention;

FIG. 2 presents the coefficients and variables of an example of alogistic regression equaiton used to predict a particular cardiacoutcome, e.g. the probability of acute cardiac ischemia;

FIG. 3 is a flow chart showing the operation of the system shown in FIG.1; and

FIG. 4 shows the record data structure which the control module storesin memory.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

Referring to FIG. 1, a cardiac patient monitoring system constructed inaccordance with the invention includes a 12-lead electrocardiograph 10,a waveform analyzer 12, a predictive instrument 14, and a control module16. Electrocardiograph 10 is connected to a patient 18 and produces aset of ECG waveforms for the patient. Waveform analyzer 12 is programmedto analyze the ECG waveforms and recognize the presence of certaincharacteristics that are particularly indicative of the cardiaccondition of the patient, e.g. the presence and elevation or depressionof S-T segments, the presence Q waves, and the presence of elevated,depressed or inverted T-waves. The particular characteristics which thewaveform analyzer is programmed to recognize depend upon the functionthat is performed by the predictive instrument which in turn determinesthe set of clinical variables that are required to perform thatfunction. Predictive instrument 14 uses the output of waveform analyzer12 in conjunction with other clinical information about the patient thathas been entered by a physician through a keyboard 22 and computes aprobability that the patient has a life-threatening cardiac condition.Control module 16 controls the operation of the other components in thesystem, detects a change in the computed probability patient'scondition, and takes appropriate actions when the detected changeexceeds certain thresholds, e.g. storing the measurements in a digitalmemory 24 (e.g. RAM or disk storage), printing a report out on a printerattached to the system, displaying the report on a video screen 28, ornotifying medical support staff when the patient's condition hasdeteriorated significantly (e.g. by using a pager 30 to send a page tothe physician).

Electrocardiograph 10 and waveform analyzer 12 are commerciallyavailable as a single unit. For example, Hewlett Packard makes the HPPagewriter XLi which is a mobile unit that can be moved from one patientto the next. The Pagewriter XLi includes a built-in 80386-based computerthat can be programmed to perform the appropriate waveform analysis. Forexample, it can be programmed to recognize and quantify the presence ofkey features within the ECG waveform. It can also be programmed toidentify the location of a myocardial infarction (MI) based on thecharacteristics of the set of signals produced by the twelve monitoringleads. Besides performing the wave analysis functions, the computerwithin the unit can also be programmed to perform the functions of othercomponents or modules within the system, e.g. the computations of thepredictive instrument and the functions of the control module.

In the described embodiment, predictive instrument 14 is an ACI-TIPI(Acute Cardiac Ischemia Time-Insensitive Predictive Instrument) whichuses a logistic regression-based equation for computing the probabilitythat the patient is experiencing acute cardiac ischemia. The logisticregression equation is of the form: ##EQU1## where P is the probabilityof acute cardiac ischemia, b_(O) is a constant, and the b_(i) 's arecoefficients of the variables x_(i) which are included in the model.

The variables which are used in this equation are shown in FIG. 2 alongwith the values of the coefficients and the values which the x_(i) 'scan take for the different variables. Note that only the largest valuefor x is used per variable. Also ECG findings must be present in atleast two leads, and S-T segment and T wave changes are "normal" ifsecondary to right or left complete bundle branch blocks, leftventricular hypertrophy, or a paced QRS. Only one type of abnormality iscoded each for S-T segment and for T wave per patient (exclusive ofTWISTDEP), use with elevation taking priority. Deviations are expressedin mm using the standard ECG scale of 1 mm=0.1 mV.

The system is programmed to operate in the manner shown in FIG. 3. Whenthe patient is first connected to the system, the physician enters therelevant clinical information about the patient (step 100). For example,if the predictive instrument is programmed to compute the probability ofacute cardiac ischemia in accordance with the above model, the physicianenters the following information about the patient: (1) name; (2) age;(3) sex; (4) whether the patient is experiencing chest or left arm pain;and (5) whether the patient's chief complaint is chest or left arm pain.After the physician has set up the system for a particular patient andconnects the leads of the electrocardiograph to the patient, thephysician causes the system to perform an initial ECG for the patient.In response, the waveform analyzer acquires and analyzes a currentsegment of the patient's ECG waveform (step 102). Typically, a 10-20second segment of the patient's ECG is required by the waveform analyzerto perform its waveform analysis functions. The output of the waveformanalyzer passes to the predictive instrument, which may, for example, beimplemented by a computation routine within the computer. In the presentembodiment, the output of the waveform analyzer reports whether: (1) anyQ waves are present; (2) whether the S-T segment is elevated ordepressed and by how much; (3) whether the T-waves are elevated invertedor flat; (4) if the T-waves are elevated, by how much; and (5) whetherboth the STDEP and TWINV leads are non-zero.

Using the output of the waveform analyzer and previously entered valuesfor other clinical variables, the predictive instrument computes aprobability that the patient has acute cardiac ischemia (step 104). Thecontrol module then stores a record of the computation in memory (step106). Referring to FIG. 4, the record includes the computed probabilityalong with the underlying clinical information, the ECG waveforms fromwhich the probability was generated, and a time-stamp indicating whenthe measurement was performed.

Thereafter, the control module enters a monitoring mode in which itperiodically executes a monitoring cycle. At the beginning of eachmonitoring cycle the system enters an idle state for a user-selectableperiod of time, T_(sec) (step 108). At the end of the idle period, thecontrol module causes the waveform analyzer to acquire and analyze asegment of the current ECG waveform and pass its results to thepredictive instrument (step 110). The predictive instrument thencomputes an updated probability for the patient based on the patient'scurrent ECG waveform (step 112). The control module computes Δ_(p) whichis measure of the change (or delta) in the patient's condition since thelast recorded monitoring cycle (step 114). In the described embodiment,Δ_(p) is equal to the proportional change in the computed probability(i.e., ##EQU2## where P_(last) equals the last recorded probability andP_(new) equals the current computed probability.

The control module compares the computed change statistic Δ_(p) to afirst threshold T.sub.Δ1, which represents an alarm threshold or thethreshold for clinical activity (step 116). That is, changes that arelarger than T.sub.Δ1 are considered to be significant enough to deservethe immediate attention of medical staff. If Δ_(p) exceeds T₁, thecontrol module stores in memory a record containing the value of thenewly computed probability, the value of Δ_(p), the underlying clinicalinformation, the corresponding ECG waveform, and a time-stamp indicatingwhen the measurement was performed (step 118). The control module alsoexecutes a notification routine which notifies medical staff of thepatient's worsening condition (step 120). The notification may be in anyone or more of many possible forms. For example, the notificationroutine might cause a page to be sent to the responsible physician, itmight generate an audible alarm at the location of the mobile monitoringunit, it might send a message to the central nursing station which wouldappear on a central display monitor and also trigger an audible alarm atthe nurse's station, or it might perform any combination of those orsimilar actions.

After the control module has executed the notification routine, itdisplays and/or prints a report of the computed probability, theunderlying ECG waveforms and a history of the computed probabilitiesover a selected period of time prior to the most recent monitoring cycle(step 122). The control module then causes the system to enter the idlestate for the preselected delay period or until the patient or medicalstaff enters new clinical information reflecting a change in thepatient's condition.

In the event that Δ_(p) is not greater than T.sub.Δ1, the control unitcompares the absolute value of Δ_(p) to a lower threshold T.sub.Δ2 (step124). Threshold T.sub.Δ2 defines a level below which the change in thepatient's condition is considered to be not large enough to beclinically significant. If Δ_(p) exceeds T.sub.Δ2, the control modulestores a record of the measurement including the computed probability,Δ_(p), the underlying data, the ECG waveform, and a time-stampindicating when the measurement was performed (step 126). This recordthen defines the reference from Δ_(p) is computed for the next computedprobability.

Threshold T.sub.Δ2 is set so that the system will generate a history ofthe patient's condition containing records of only the clinicallymeaningful events. Using the second threshold as a criteria for storingmonitoring data greatly reduces the amount of information that is storedby the monitoring system. Thus, the limited system memory is not filledwith clinically redundant information.

After a monitoring cycle is complete, the system returns to an idlestate for the preselected period of time. Then, it begins a newmonitoring cycle. The above-described sequence repeats until the userterminates it.

As alluded to above, it is possible to manually trigger a monitoringcycle by entering new values for relevant clinical variables. Forexample, if the patient begins to experience chest pain, he can enterthis new information through a bedside console 32 (see FIG. 1), oralternatively, medical staff can enter it through another input device.Entering the changed clinical information causes the control unit toimmediately initiate a new monitoring cycle immediately or, if it iscurrently in the middle of a monitoring cycle, to initiate a monitoringcycle immediately after it has completed the current monitoring cycle.

The system also has a manual mode of operation in which the probabilityis computed only when medical staff manually initiates a new monitoringcycle by, for example, pushing an appropriate button on the bedsidecontrol console. In all other respects, the monitoring cycle which isinitiated is the same as the monitoring cycle described above.

It should be noted that the thresholds, T.sub.Δ1 and T.sub.Δ2, may besimple fixed value thresholds or they may be functions of some othervariable, such as P_(last). For example, with regard to T.sub.Δ1 it maybe desirable to have this value set higher for smaller values of Δ_(p)than for larger values of Δ_(p). In other words, a 25% increase from aninitial probability of 5% would probably not be significant whereas a25% increase from an initial probability of 40% would probably provide abasis for real concern.

In addition, the thresholds can be a function of previous computedchanges. For example, two successive Δ_(p) 's both indicating aworsening condition provide a greater basis for concern than does asequence of Δ_(p) 's that alternate between positive and negativevalues. In other words, the threshold could be set as a function of thedirection (or sign) of computed change for previous monitoring cycles.If the previous computed value for Δ_(p) was positive (e.g. indicating aworsening condition), the threshold for the next computed value of Δ_(p)might be lower than it would be if the previous computed value for Δ_(p)was negative.

Also Δ_(p) can be defined in any of a number of alternative ways. Forexample, it can be a rate of change computed by the change from the lastrecorded clinically meaningful event divided by the time differencebetween the current measurement and the last recorded measurement.Alternatively, it can simply be an absolute change (i.e., P_(new)-P_(last)).

The logistic regression equation presented above is presented as merelyillustrative of one way in which the cardiac condition and theprobability can be modeled. There are a variety of statistical methodsand algorithms that can be used for calculating the predictedprobability of an adverse cardiac event or a life threatening cardiaccondition. These include, for example, artificial feed-forwardmultilayer neural networks and classification trees. Although theprocedures may differ among these various techniques, many of them maygenerally be expressed in terms of finding an optimal solution to thefollowing general equation: ##EQU3## where f⁻¹ and g_(i) are generalfunctions, the X_(n) 's (where 1≦n≦p) are the values of the independentinput variables, and β_(O) and β_(i) (where 1≦i≦K) are modelcoefficients.

The standard logistic regression equation described earlier can bederived from the above equation. In addition an artificial network witha single hidden layer and an activation function "h" can be written as:##EQU4##

The automatic, periodic monitoring of the patient's condition with thepredictive instrument provides information that assists the physician inmore accurately evaluating the seriousness of the patient's conditionand in obtaining early detection of changes in the patient's condition.The advantages are illustrated by the following actual histories of fourpatients, identified as Patient A, Patient B, Patient C, and Patient D.

Patient A presented at the ED (Emergency Department) with a potentiallylife-threatening cardiac rhythm. The first computed probability of acutecardiac ischemia (i.e., either unstable angina pectoris or acutemyocardial infarction, as opposed to a rhythm disturbance) wasrelatively low, at 10%. In a subsequent ECG taken 23 minutes later, thecardiac rhythm had returned to normal, which would lead to a conclusionof no remaining cardiac problem. Indeed, a usual ECG monitor would haveshown the patient as having a normal rhythm and an essentially normalECG. However, the ECG contained a relatively miner ST elevation in theanterior leads which caused the computed probability of acute cardiacischemia to rise to 16%, representing a 60% increase from the previouscomputed probability. An increase of that amount flagged the patient ashaving acute cardiac ischemia and still requiring acute medicalattention.

An interesting aspect of this example is that both of the computedprobabilities are below the level at which patients are commonly senthome, which in the facility in which this patient was treated, is about18%. Thus, a computed probability of 16% by itself is difficult tointerpret because the physician does not know whether that is a normalreading for that patient. Just being of a certain age and gender andhaving chest pains can often result in more than 16% probability with noabnormality in ECG.

To more fully appreciate the significance of a computed probability of16%, it is useful to explore what the figure actually means. A 16%reading indicates that among a representative population of 100patients, all of whom arrive at the ED with presenting conditions thatproduce a probability of 16%, on average 16 of the patients will haveacute ischemia and 84 patients will not have acute ischemia. Within thatpopulation, however, the predictive instrument based upon a singlecomputation is not able to distinguish between the group of patients whohave acute ischemia and the group of patients who do not have acuteischemia. By using the predictive instrument in accordance with theabove-described invention, the physician is able to discover furtherinformation which will assist the physician in determining to whichgroup the patient actually belongs.

By monitoring the change in the patient's computed probability, thephysician was able to gain further information which helped interpretthe initial reading of 10%. That is, the instrument detected asubstantial change in the computed probability in a direction thatflagged the patient as being more likely to fall within that category ofpatients who have acute ischemia.

In the case of Patient B, he arrived at the ED with a chest pain as hischief complaint. His initial computed probability of acute cardiacischemia was 25%. However, chest pain in a properly aged male, who doesnot have acute cardiac ischemia, can still produce a relatively highprobability. In other words, one can obtain that same probability in avariety of different ways, not all of which are symptomatic of acutecardiac ischemia. Thus, even though the presenting probability of 25% isa relatively high number, by itself its meaning to the physician isstill ambiguous. It simply indicates that the patient falls into acategory of patients who have historically exhibited higher risk thanthe group of patients who have generated lower computed probabilities.From the physician's perspective the predictive instrument is indicatingthat the patient has a one-in-four chance of having acute cardiacischemia but the instrument is also indicating that the patient has athree-in-four chance of not having acute cardiac ischemia.

In the case of Patient B, a second ECG approximately one hour laterstill appeared to be relatively normal and again produced a computedprobability of acute cardiac ischemia of 25%. However, in a subsequentelectrocardiogram taken about two hours after the initial ECG, therewere very subtle changes of lateral T wave flattening that produced acomputed probability of 37%, clearly flagging the patient as requiringclinical activity. Such subtle changes would not have been flaggedexcept for the fact that the predictive instrument magnifies any changesof this type. Thus, because the predictive instrument when used in acontinuous manner amplifies the seemingly most trivial of changes in thepatient's condition, it can give the physician further valuableinformation which will help him or her to accurately evaluate thepatient's condition and make the correct admission decision.

Patient C, a woman with known cardiac problems, came into the ED withgeneral complaint about not feeling well. When the monitor was connectedto her, the computed probability was 10%. Nevertheless, because of herhistory of known problems she was admitted to the ward. While she was inthe ward, she began to experience chest pains which caused the computedprobability to jump up to 50%, an increase of about 400%, clearlyflagging her for immediate clinical action.

Patient D, a 64 year old woman, represents yet another example of theinvention's ability to help medical staff distinguish among patient'swho have acute cardiac ischemia and those who do not. The patient'sinitial ECG was benign and the computed probability indicates only a 4%likelihood of acute cardiac ischemia. She was admitted to the wardbecause she was known to have bad coronary disease. In a subsequent ECGtaken about 22 hours later, suddenly the very same ECG, coupled with anew chief complaint of chest pain, resulted in a six-fold increase inthe probability of acute cardiac ischemia (i.e., 24%). In this case, theactual ECG was unchanged and thus a normal ECG heart monitor would nothave picked up the significance of the new information. Moreover, thispatient's final number (i.e., 24%) was below average for patients in theED and, in fact, was within the range of numbers for patients who aresent home. However, because the delta (i.e., change in computedprobability) was substantial she was flagged as a patient requiringfurther clinical activity.

Other embodiments are within the following claims. Even though themonitoring of the probability of acute cardiac ischemia for changes wasused as an example, this was not meant to limit the scope of theinvention to only those systems which compute that particularprobability. The invention also encompasses systems which periodicallycompute and monitor for changes any probability of a serious cardiaccondition. In addition, the method by which the probability is notcomputed is not central to the invention. Any approach which attempts tomodel historical patient information to compute a probability of thepatient having a serious cardiac condition based at least in part uponECG information falls within the scope of this invention.

What is claimed is:
 1. A system for continuously monitoring thecondition of a patient who has a cardiovascular disease, said systemcomprising:an electrocardiograph which during use is connected to thepatient; a waveform analyzer which analyzes a segment of an ECG waveformgenerated by the electrocardiograph for the patient; a predictiveinstrument receiving output from the waveform analyzer, said predictiveinstrument programmed to complete a monitoring cycle in which thepredictive instrument uses the output from the waveform analyzer tocompute a probability of a life threatening cardiac condition based uponthe segment of the patient's ECG waveform; and a detector module whichreceives computed probabilities from the predictive instrument for asequence of monitoring cycles and for each monitoring cycle isprogrammed to compute a change-of-condition measure, wherein saidchange-of-condition measure is calculated by subtracting a computedprobability for a previous monitoring cycle from the computedprobability for a current monitoring cycle, said detector module furtherprogrammed to compare during each monitoring cycle the computedchange-of-condition measure for that monitoring cycle to a thresholdvalue and if in excess of said threshold value to generate an alarmnotification.
 2. The system of claim 1 wherein the detector module isfurther programmed to cause said predictive instrument to perform saidsequence of monitoring cycles.
 3. The system of claim 2 furthercomprising a computer and wherein the waveform analyzer and thedetection module are both implemented by said computer.
 4. The system ofclaim 1 further comprising an input device for entering clinicalinformation relating to the patient and wherein said predictiveinstrument uses the output from the waveform analyzer as well as theclinical information entered through said input device to compute theprobability of a life threatening cardiac condition.
 5. The system ofclaim 1 wherein the probability of a life threatening cardiac conditionis a probability that the patient has acute cardiac ischemia.
 6. Thesystem of claim 1 wherein the probability of a life threatening cardiaccondition is a probability of acute hospital mortality for the patient.7. The system of claim 1 wherein the predictive instrument uses alogistic regression equation to compute the probability of a lifethreatening cardiac condition.
 8. The system of claim 1 wherein thecontrol module is programmed to cause said computer to repeat themonitoring cycle at regular intervals separated by a preselected delay.9. A method for continuously monitoring the condition of a patient whohas a cardiovascular disease, said method comprising:using anelectrocardiograph to generate an EKG waveform for the patient;analyzing said EKG waveform; based on analyzing said EKG waveform,periodically computing a probability of a life threatening cardiaccondition based upon the patient's ECG waveform; from the periodicallycomputed probability, periodically computing a change-of-conditionmeasure, wherein said change-of-condition measure represents adifference between a previously computed probability and a more recentcomputed probability; periodically comparing the periodically computedchange-in-condition measure to a threshold value; and if any computedchange-in-condition measure exceeds said threshold value, generating analarm notification.